Understanding the odour network
© Kumar et al; licensee BioMed Central Ltd. 2014
Published: 16 April 2014
We live in a sea of data which we interact, inadvertently or advertently, through our senses. Among major sensory modalities, the physical stimulus attributes to perception mapping is more or less well defined in vision and audition, but not in olfaction. Although, the molecular concentration to perceived odor intensity mapping has been established , there is no known general systematic relationship of molecular properties onto the olfactory percept. In other words, it has been a challenge to predict the smell of a novel molecule by its physicochemical structure, or the physicochemical structure of a novel smell. There has been a large progress in this decade towards understanding the molecular and neural basis of olfactory perception , , , , , . Khan et. al predicted the odor pleasantness based on odorant structure and they considered odorant database of 144 odorants . Kermen et. al  reported the relationship between olfactory note and molecular structure recently. The knowledge of chemistry, odor scientists and efforts of IBM  has given us huge database of almost 31 million molecules along with their physical, structural and other properties. This dataset is not structured in terms of odor, and no attempt has been made to understand and organize this huge data in olfaction space. One of the most important aspects of understanding the data is visualization. Recently, network analysis has grabbed much attention due to its clear representation in terms of entities and relationship and more often than not, they provide some really interesting insights into the data they represent . The present work aims at discovering inherent statistical structure in large chemical and perceptual databases available online in order to derive principles for predicting odor perception from the chemical structure of odorants via network analysis of Flavournet dataset. The Flavournet dataset consists of 738 odorants arranged by chromatographic and sensory properties. An adjacency list of odorants on the basis of perceived smell was created. Further, an odor network was created in which each odorant forms the node and weight of edge between them shows the number of odors they share with each other. The initial results give very good insights about the dataset such as there are two islands in this network. The fully connected smaller island is only inhabited by alkane family of odors and the bigger island follows the degree distribution of a scale free network. Further, the physical and structural descriptors were superimposed on this graph in order to understand it in a better way. The results provide useful insights into the odor space.
- Cain W: Odor intensity: Differences in the exponent of the psychophysical function. Percept Psychophys. 1969, 6: 349-354.View ArticleGoogle Scholar
- Lavine BK, White C, Mirjankar N, Sundling CM, Breneman CM: Odor-structure relationship studies of tetralin and indan musks. Chem Senses. 2012, 37: 723-36.View ArticlePubMedGoogle Scholar
- Zarzo M: Hedonic judgments of chemical compounds are correlated with molecular size. Sensors (Basel). 2011, 11: 3667-86.View ArticleGoogle Scholar
- Hasegawa T, Izumi H, Tajima Y, Yamada H: Structure-odor relationships of α-santalol derivatives with modified side chains. Molecules. 2012, 17: 2259-70.View ArticlePubMedGoogle Scholar
- Czerny M, Brueckner R, Kirchhoff E, Schmitt R, Buettner A: The Influence of Molecular Structure on Odor Qualities and Odor Detection Thresholds of Volatile Alkylated Phenols. Chem Senses. 2011, 539-553.Google Scholar
- Schmuker M, de Bruyne M, Hähnel M, Schneider G: Predicting olfactory receptor neuron responses from odorant structure. Chem Cent J. 2007, 1: 11-PubMed CentralView ArticlePubMedGoogle Scholar
- Schmuker M, Schneider G: Processing and classification of chemical data inspired by insect olfaction. Proc Natl Acad Sci. 2007, 104: 20285-20289.PubMed CentralView ArticlePubMedGoogle Scholar
- Khan RM, Luk C-H, Flinker A, Aggarwal A, Lapid H, Haddad R, Sobel N: Predicting odor pleasantness from odorant structure: pleasantness as a reflection of the physical world. J Neurosci. 2007, 27: 10015-23.View ArticlePubMedGoogle Scholar
- Kermen F, Chakirian A, Sezille C, Joussain P, Le Goff G, Ziessel A, Chastrette M, Mandairon N, Didier A, Rouby C, Bensafi M: Molecular complexity determines the number of olfactory notes and the pleasantness of smells. Sci Rep. 2011, 1: 206-PubMed CentralPubMedGoogle Scholar
- IBM News room: IBM Contributes Data to the National Institutes of Health to Speed Drug Discovery and Cancer Research Innovation - United States. IBM news. 2011Google Scholar
- Ahn Y-Y, Ahnert SE, Bagrow JP, Barabási A-L: Flavor network and the principles of food pairing. Sci Rep. 2011, 1: 196-PubMed CentralView ArticlePubMedGoogle Scholar
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